Comparison of support vector machine and neutral network classification method in hyperspectral mapping of ophiolite mã©langes–a case study of east of iran

Comparison of support vector machine and neutral network classification method in hyperspectral mapping of ophiolite mélanges–A case study of east of Iran The Egyptian Journal of Remote Sensing and[.]

Review Article

Comparison of support vector machine and neutral network

classification method in hyperspectral mapping of ophiolite mélanges–A

case study of east of Iran

Department of Geology, Shahid Bahonar University of Kerman, Iran

a r t i c l e i n f o

Article history:

Received 30 November 2014

Revised 26 December 2016

Accepted 19 January 2017

Available online xxxx

Keywords:

Ophiolite mélanges

Hyperion

Support vector machine

Neutral network analysis

East of Iran

a b s t r a c t

Ophiolitic regions are one of the most complex geology settings Mapping in these parts need broad and precise studies and tools because of the mixture rocks and confusion units Hyperion hyperspectral sen-sor data are one of the advanced tools for earth surface mapping that containing rich information of shal-low electromagnetic reflection in 242 continuous bands Because of some contaminated noise in tens of these bands we removed 87 most noisy bands and focused our study on 155 low noisy bands In present study, tow spectral based classification algorithms of support vector machine and neutral network are compared on hyperion image for classification of cluttered units in an ophiolite set Study area is Mesina region in collision ophiolitic belt of south east of Iran In this region for design processing results validation rate, lots of random locations and control points were studied in field scale and were sampled for laboratory surveys Samples were investigated in microscopic section and by electron microprobe sys-tem Based on laboratory-field studies, the lithology of this area can divided into five general groups: (Melange series, metamorphic units, Oligocene – Miocene to Quaternary volcanic units, lime and flysch units) Based on field-laboratory works, some standard points defined for validate processing results accuracy rate Therefore, the Support Vector Machine and neutral network method as advanced hyper-spectral image processing methods respectively have overall accuracies of 52% and 65% Consequently the method based neutral network theory for hyperspectral classification have acceptable ratio in sepa-ration of blended complicated units

Ó 2017 National Authority for Remote Sensing and Space Sciences Production and hosting by Elsevier B.V This is an open access article under the CC BY-NC-ND license (

http://creativecommons.org/licenses/by-nc-nd/4.0/)

Contents

1 Introdution 00

1.1 Hyperion sensor 00

1.2 Previous studies 00

1.3 Geological setting 00

2 Materials and methods 00

2.1 Preprocessing of data 00

2.2 Classification using SVM 00

2.3 Classification using neural network method 00

3 Sampling method and laboratory studies 00

3.1 Ophiolite mélange 00

3.2 Oligo – miocene volcanic 00

3.3 Metamorphic units 00

3.4 Sedimentary rocks 00

http://dx.doi.org/10.1016/j.ejrs.2017.01.007

1110-9823/Ó 2017 National Authority for Remote Sensing and Space Sciences Production and hosting by Elsevier B.V.

This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ).

Peer review under responsibility of National Authority for Remote Sensing and Space Sciences.

⇑ Corresponding author.

E-mail addresses: B.Bahram.100@gmail.com (B Bahrambeygi), Hesammmoeinzadeh@yahoo.com (H Moeinzadeh).

Contents lists available atScienceDirect

The Egyptian Journal of Remote Sensing and Space Sciences

j o u r n a l h o m e p a g e : w w w s c i e n c e d i r e c t c o m

4 Results and discussion 00

4.1 Data analysis 00

4.2 Validation by field observations 00

4.3 Processing accuracy 00

5 Conclusion 00

References 00

1 Introdution

Ophiolite mélanges of Iran represent a part of an ophiolite belt

extending from Pakistan via Iran to Turkey, Greece and some other

countries in Europe (Weber Diefenbach et al., 1984; Ghazi et al.,

2004) The Iranian ophiolites are part of the orogenic sutures

mark-ing the diachronous closure of the Tethyan oceanic realms

(Palaeo-tethys and Neo(Palaeo-tethys) along the Alpine–Himalayan convergent

front running from the Mediterranean through East Europe, Middle

East to Asia (Arvin and Robinson, 1994) (Fig 1a) In particular,

var-ious ophiolitic sutures surround the Central East Iranian

Microcon-tinent (Rossetti et al., 2010) (Fig 1b) Ophiolitic mélanges are of

special importance for some important mineralizations and

addressing the temporal framework of the paleotectonic evolution

(Gilbert and Park, 1997 and Gomes-Pugnair et al., 2003 and

Brocker et al., 2011) Ophiolites of the studied area located in

col-lision belt are so much complex and tectonized Mapping and

dis-tinction lithology units in these geology settings are very difficult

Hyperspectral mapping is a new technology using spectral

behav-iors could be useful as an economical method to mapping and

dis-tinguishing of complex lithologies with satellite images

(Alavipanah, 2003) The hyperspectral methods are based on

Spec-troscopy, and Spectroscopy is based on the facts that interaction of

surficial molecular structure of a substance with electromagnetic

waves impinging on it (Clark et al., 1990; Gupta, 2003) Natural

substances constituting the Earth’s surface will absorb, reflect or

scatter the electromagnetic waves according to their composition

(Crowley and Clark, 1992; Sabins, 1997) It is possible to determine

the spectrometric response of different substances such as

miner-als at the form of continuous curves in a broad spectrum of

electro-magnetic waves (Clark and Swayze, 1995) These curves are used

as symbols for identification of different substances and their

com-position (Clark, 1993) Hyperspectral sensors are capable to image

in numerous extremely narrow spectral bands (Kruse et al., 1993 andKruse et al., 2002andKruse et al., 2003a,b) Spectral curves with a good spectral resolution could be used for determination

of absorption characteristics of substances with little differences

in spectral characteristics (Moeinzadeh et al., 2013) Split of lithol-ogy surfaces and mapping of ophiolite mélange units is generally puzzling because they are very mixture and unruly in properties

In future, hyperspectral mapping along with limited field inspec-tion can simplify ophiolite mapping

1.1 Hyperion sensor Hyperion represents the first airborne hyperspectral sensor mounted on EO-1 platform Hyperion images are taken in 242 nar-row bands in wavelengths between 356 and 2577 nm with 10 nm spectral resolution (USGS, 2004) These images were swiftly used

in geological investigations Hyperspectral data may be used for studying spectral patterns of surficial materials Hyperion sensor utilizes push broom Technology and an area expending 7.7 km orthogonal to the movement is imaged So, spectral data pertaining

to diverse materials and features on the surface of the Earth are recorded as three dimensional cubic frames (Remote Sensing Tutorial of NASA, 2017) of the total 242 imaging bands using by Hyperion sensor, only 198 bands are calibrated and are usable for Processing operations Spatial resolution of Hyperion is 30 m and each image includes a narrow band 7.7 km in width and 185

or 42 km in length (Pearlman et al., 2003)

1.2 Previous studies Hyperion hyperspectral images have been used in agriculture, mineral exploration, separation of land units as well as other fields

of geological sciences For example, Kruse et al (2003a,b)have

Fig 1 Distribution map of Mesozoic ophiolite belts of Iran ( Fotoohi Rad et al., 2009 ).

compared the capability of airborne hyperspectral data of

Hyper-ion for spectral separatHyper-ion of land surface minerals Hubbard

et al (2003)have compared mineral alteration mapping of visible

to shortwave infrared Hyperion with ALI and ASTER image views

Also, using EO-1 Hyperion images,Kruse et al (2003a,b)have

pre-pared the hyperspectral map of coral reefs of Buck Island in central

Atlantic Ocean And, using EO-1 satellite data,Beiranvand Pour and

Hashim (2011)have prepared the geological map of the

southeast-ern part of the central Iranian Volcanic Belt.Abou El-Magd and

El-Zeiny (2014) studied water quality using hyperspectral data

Ramadan and Abdel Fattah (2010)tried to Characterization of gold

mineralization using Hyperion images.AbdelRahman et al (2016),

use of Hyperion images to producing the soil map Some other

rel-evant studies using hyperspectral data in geological investigations

includeCoops et al (2002), Staenz et al (2002), Pearlman et al

(2003), Datt et al (2003), Felde et al (2003), Bindschadler and

Choi, (2003), Goodenough et al (2003), Ramsey et al (2004),

Khurshid et al (2006), Gersman et al (2008)), Leverington

(2008), San and Suzen (2010), Sarup (2011)andShokr (2011)

Geo-logical investigation undertaken in the studied are an include

Fotoohi Rad et al (2009), Brocker et al (2011), Theunissen et al

(2011) and Honarmand et al (2012) Fotouhirad (1996) and

Fotouhirad (2004) studied area as aspect of petrology however,

no remote sensing studies have taken place in this area up to

now, and the present study is the first one to employ hyperspectral

data for separation of ophiolite mélanges

1.3 Geological setting

The studied area lies in the structural zone of sabzevar-Sistan

which formerly was described byTirrul et al (1983) In this zone,

volcanic and plutonic igneous rocks are widespread calk-alkaline

volcanic rocks aging late cretaceous-Paleocene are observed in

the eastern and northeastern Part of Sistan region and they have

been ascribed to subduction of an oceanic Plate under the Afghan

Block (Tirrul et al., 1983) Among the volcanic rocks aging

Eocene- Pliocene in this zone, Eocene – Oligocene volcanic

includ-ing Porphyry andesites, Pyroclastic and dacitic lavas are much

more common The oldest volcanic rocks which have been named

‘‘Cheshmeh Ostad Group” (Tirrul et al., 1983) are ophiolitic in

char-acter, although lack ultramafic and layered gabbro Cheshmch

Ostad intrusive as well as calk-alkaline intrusive aging upper

Eocene- lower Oligocene (including Zahidan granite) have intruded

into slightly – metamorphosed marine detrital deposits of Neh

complex The youngest volcanic activities in Sistan structural zone include Quaternary olivine basalts which cover older units in the northern Part of this zone The studied ophiolite mélange is inter-mingled with flysches which are partly metamorphosed, so that a major Part of the ophiolites has been metamorphosed There is a conspicuous metamorphosed zone in the eastern Part of Eastern Iran ophiolites comprising green schist epidote amphibolite, amphibolite, blue schist and eclogites This metamorphic zone is very conspicuous (Fotoohi Rad et al., 2009) Such rocks play a key role in recognition of the tectonic environment and evolution

of orogeny belts and commonly represent locations of oceanic crust seduction before collision of continental crusts (Bucher and Frey, 1994) Oligocene – Miocene volcanic activities in eastern Iran include dacites, riodacites, andesitic dacites, porphyroidal quartz-diorites and andesitic basalts which commonly lie at the higher Parts of the region

2 Materials and methods 2.1 Preprocessing of data Preprocessing of data taken from Hyperion sensor include orga-nization of bands in a form of Process able digital data, calculation

of the median wave length of spectral bands and putting it in its right wave length, recognition of plotted bands, removal of anoma-lous data, geometric correction, erasing strip lines in image bands using Kernells and, finally, atmospheric correction In organization and filtration of image bands, 87 bands of the total 242 imaged bands wave wiped out due to unsuitable quality of data, So 155 bands were studied Geometric correction was undertaken by images of Quick bird satellite mounted on the Global Positioning system (GPS) and via field studies Atmospheric correction of Hyperion data was performed using Internal Average Relative Radiance (IARR) or relative average of reflectance as a suitable pre-processing for recovering spectral information on hyperspectral data in a semi-arid region

2.2 Classification using SVM The support vector Machines (SVM) method is a nonparametric and controlled statistical method and acts upon the premise that type distribution of data sets is unknown The main character of this method is its high capability in using trained samples and attaining higher accuracy in comparison with other methods of classification (Mantero et al., 2005andMountrakis et al., 2011)

In reality the support vector machine is a binary classification which separates two classes by a linear boundary and relies on extended linear SVM classifies the data by passing a plane (linear boundary) and by using all bands and employing an optimization algorithm, so that samples forming the boundaries of classes are determined In another words, a number of training points which are nearest to decision border are considered as support vectors

In this method, increasing the dimensions of data leads to better results In reality, if in read space the classes interfere, the data are carried to a larger space so that their differentiation becomes possible In this algorithm, the main purpose is to find the farthest distance between two classes which leads to more accurate classi-fication, while generalization error decreases (Zhang et al., 2008) The main distinguishing component of SVM is the trend of this algorithm on a rule which is known as a structural Risk Minimiza-tion (SRM) In reality, the SVM minimizes the classificaMinimiza-tion errors

in unobserved data lacking The premise of the possible destruction

of data, while statistical techniques such as MLC consider the data destruction as ‘‘known” (Mountrakis et al., 2011) The optimum border is used for determination of decision border at each

Fig 2 SVM method to classify the two classes using a linear kernel in two

dimensions ( GoodarziMehr et al., 2012 ).

completely- separated two classes (Vapnik and Chervonenkis,

1991) The linear border between the two classes is completed so

that:

a) All samples belonging to -I class are located in one side of

the border and all samples belonging to -1 class are located

in the other side

b) The decision border must be selected so that distance

between the training samples and each couple of classes in

orthogonal direction becomes as maximized as possible with

respect to decision border In this method, firstly, the

dis-tance between the nearest training samples of the two

adja-cent classes is orthogonal direction with respect to borders

in calculated and optimized border Which contains the

lar-gest border is determined Two parallel planes are defined in

the two sides, of decision border, so that the border plane

contains the largest equal distance with respect to these

two plains Generally, the more the distance between two

parallel planes the higher the accuracy of classification

(Srivastava and Bhambhu, 2009) Actually, this algorithm

seeks to find a super plane which can act so that while being

compatible with training data, can separate the data set

from each other (Mountrakis et al., 2011)

A suitable super plane is a separator which makes it possible to

maximize the widths so that no pixel can place in between

(GoodarziMehr et al., 2012) The optimized separating super plane

term refers to a zone which, by using training data, makes it

pos-sible to minimize the pixels which are classified uncorrectly

(Mountrakis et al., 2011) There are several Kernells for defining

this border plane (Fig 2) Whenever the super data contain too

much interference it is possible to use multi term Kernels with

dif-ferent terms and gammas or use Radial Basis Function (RBF)

Ker-nel The pertaining equations for these three Kernels are the

following:

iXj

iXjþ rÞd; g > 0

In the above equations, T represents transposed matrix, G

gamma/d represents the degree of multi term and Xjand Xi

repre-sent the Vector components i and j In this study, classification of

lithological units was conducted using the above–mentioned three

Kernels and the degree of polynomial and different gamma values

Afterwards, the results were analyzed Really in nonlinear SVM Kernels, gamma parameters control the form of decision border its low values get the decision border tend to linear situation with increasing its values, the flexibility of decision border increases and closes to the form of super data of each class Changes in d param-eter increase the flexibility of the separating super plane 2.3 Classification using neural network method

To date, various approaches have been proposed for artificial neural networks that one of most common is multilayer percep-tron neural network (Ahmadi Nadushan et al., 2009) Performance based of network classification method is specified when the data set we have are not separated in this manner by a simple linear decision surfaces and by such methods, and layered in a process

of using non-linear levels, the differentiation be possible (Richards, 1999) In fact, these methods are characterized by strat-ified layers, each layer formed of nodes (neurons) and by a multi-input, process started and lead to output (Richards, 1999) Summary of performance of this method is based on the follow-ing equation (Fig 3):

In the above equation represents the thresholdh, W is a vector

of weighting coefficients and x is the input vector The number of neurons is specified by network topology and data dimensions (Richards, 1999) The number of input neurons was used to classify the 158 reflective bands of Hyperion sensor Classification process was performed by using neural networks in three stages as follows:

1 - The first phase of the training process using input data

2 - The success of the first phase of the validation and verification

of network would (produce the graph W RMS values obtained for n iterations)

At this stage, with 10% training data and repeated of 350 times RMS less than 5.0 was given, but with 50 and 100% of training data, respectively, with 100 and 50 times of repeating RMS values were close to their minimum very quickly The adverse reactions were classified in the training set (Wijaya, 2005)

3 Sampling method and laboratory studies According to the field studies undertaken by authors as well as the geochemical mineralogical, geothermobarometric and geochronologic studies undertaken byFotoohi Rad et al (2009), Brocker et al (2011), Theunissen et al (2011)andBröckera et al (2013), the rocks units of the studied area are classified into five general groups Also in several field traverses, all rock units were sampled Accordingly the igneous rocks may be divided into two general groups (1) units related to ophiolite mélange and (2) oligo – miocene volcanic complex

3.1 Ophiolite mélange This unit is composed of (1) magmatogenic units of ophiolitic sequence such as peridotites, gabbro, microgabros, diabases and plagiogranites and (2) secondary units created from metamorphism and alteration of magmatogenic units which include metaperidotites, metagabbros, serpentines, milonitized metaplagiogranites and listvinites The main characteristics of these units are presented inFig 4depicts some microscopic and field image of them

Fig 3 Mode of action classification using neural networks.

3.2 Oligo – miocene volcanic

These volcanic lie in the form of a magmatic arc in the east of the

studied area and follow the general trend of the region (Fotoohi Rad

et al., 2009) According to theTirrul et al (1983), crystallization of

these rocks which also lie in Nehbandan quadrangle map in younger

than igneous rocks comprising ophiolite mélange and belong to

vol-canic activities in upper cretaceous, oligo – miocene and Quaternary

times in eastern Iran They also include andesites to andesitic

basalts of oligo – miocene time In accordance with pyroclastic,

andesite, porphyritic andesite and andesitic basalts are usually observed as large outcrops and comprise high mountains In por-phyry andesite plagioclases, hornblendes, and biotitic are observed

as coarse crystals and phenocrysts in a ground mass composed of plagioclase microlites and small crystals of amphiboles and opac minerals In the samples, plagioclases are altered into serisite and carbonate and to a lesser amount to kaolinite and epidote Their tex-ture is almost porphyritic It is worth mentioning that one of the main differences between these rocks with andesitic basalts is the lack of olivine and clinopyroxene in them (Fig 5)

B

F D

C

E A

Fig 4 A-sub ophitic to granular texture on isotropic gabbro (XPL) B-Abundant plagioclase Plagiogranite belonging to the ophiolite complex (XPL) C-Microscopic images of silica Lisstwenite (XPL) D-listwenitization of peridotites (View of the West) E-Isotropic gabbro and listwenitization peridotite and sequence of Paleocene – Eocene limestones on them (view to north) F-white Plagiogranite cropped (away) and peridotite and the metamorphic zone border (near) (see the West).

A

1 mm

Fig 5 Microscopic images of rock samples: A – andesite – amphibole of the opacities, B-diorite porphyry, C-andesite basalt – the presence of olivine and pyroxene as phenocrysts in the background of microlitic plagioclase XPL.

Metamorphic

Zone

A

Fig 6 The remarkable extent of metamorphic units (see the North East), B-sight near the amphibolite schist with copper mineralization, C-schistosity in rocks : greenschist Xpl.

3.3 Metamorphic units

Although outcrops of metamorphic rocks are observed in all

parts of the studied area, but the majority lie in the metamorphic

rocks at the east of ophiolite mélange Scattered outcrops are

observed in other part of the ophiolite unit In this metamorphic

zone, flysches and the rocks related to ophiolitic complex which

predominantly have been mafic and ultramafic are

metamor-phosed The main facieses include green schist (including talk

schist) facies, epidot- amphibolite schist (including epidote

amphi-bolites and epidote- amphiamphi-bolites schist) facies – amphibolite

facies (including amphibolites and garnet – amphibolite schist;

Fig 6)

3.4 Sedimentary rocks

Although, in comparison with igneous and metamorphic rocks,

the sedimentary rocks are less common and diverse however there

are several scattered units of this kind in the studied area which

include (1) Paleocene-Eocene limestones which outcrop in the

eastern part a the area, (2) micritic and sapary limestone, cherts

and radiolarites intermingled with ophiolite mélange and flysches

composed of siltstones, fine sandstones and cherty shales which

are predominantly metamorphosed

4 Results and discussion

4.1 Data analysis

Algorithm analysis in processing of hyperspectral data byKruse

et al (2003a,b)andLeverington (2008)tested to the higher

effi-ciency of processing which are based on spectral pattern in

com-parison with those which are based on statistical models So, in

order to determine the potential of hyperspectral data to separate

ophiolite mélanges, the SVM algorithm was selected and small area

of five general lithology were considered for SVM analysis In this respect the reflectance pattern of several rock units was used as mixed spectrum of index pixels for training points For every litho-logical pattern were determined in images And eventually, accord-ing toClark and Swayze (1995)in the histogram of output image those pixels which The Whiteness value laying the upper bound average plus two times of standard deviation were selected as favorable pixels and presented as vector data.Fig 7extracted from SVM processing andFig 8extracted from NUT processing method Fig 9is the part of the map presented byFotoohi Rad et al (2009) with 1:20,000 scales Visual comparison of the processed image with geological map of the area represents a favorable conformity

in the majority of parts It should be noted that current geological map is prepared in a very smaller scale and less accuracy than the processed images In continue the results of hyperspectral process-ing will compare with field studies

4.2 Validation by field observations

In order to access the separation accuracy coefficient and recog-nize the SVM method on Hyperion image of the area, the enhanced zones were indexed as vector data on Quick bird image of the area and were evaluated in field studies Also for computing the accu-racy coefficient of processing factor, considering the discontinuity

of rock units in ophiolite mélange, Criterion accuracy of image was determined by control points using sampling points Since band widths in hyperspectral sensors is narrow and very thinner than multispectral one the energy supply of receiving waves by sensor is necessarily taken place from more wider spaces As a result, the hyperspectral images lack high spatial resolution (Alavipanah, 2009) In field studies, in order to increase the accu-racy and clarity of traverses, vector maps resulting from the pro-cessing of Hyperion image were imposed on a Quick bird image

Fig 7 Hyperion image processing area on the output map of SVM method.

Fig 8 Hyperion image processing area on the output map of NUT method.

Fig 9 Part of Tabas Messina area map, 1:20,000 from Fotouhirad (2004)

Fig 10 The location of sampling points on the band of 98 in Hyperion image.

Table 1

Supervised classification accuracy matrix of the optimal pixels in the SVM image processing method.

Table 2

Coefficient of user accuracy and producer accuracy on optimal pixel in the SVM image processing method.

Table 3

Supervised classification accuracy matrix of the optimal pixels in the NUT image processing method.

having 60 cm spatial resolution using GIS technique These maps

which were introduced into a GPS were used as guides to the sites

indicated in processing of Hyperion image Also during field

stud-ies, coordinates of the sampling points (Fig 10) were determined

on Hyperion image and the samples were classified into five

groups: ophiolite mélange, metamorphic units, Oligocene Miocene

volcanic, flysches and lime stones The coordinates of sampling

points were set on Hyperion image as vector data and location of

pixels encircling the points indicated as training data on Hyperion

image was defined and indexed as the class of each lithology in

through image

4.3 Processing accuracy

Controlled classification present a digital basis for quantitative

comparison of the results taken from image processing and field

data in the form of zones limited to pixels having proper values

The accuracy matrix of indexed pixels in classification and the

sampled points in field and laboratory studies (Table 1 and 3) were

determined by implementing controlled classification methods for

pixel data resulted from processing by SVM method on Hyperion

image of the studied area The digital basis of comparison in

con-trolled classification method may expressed by factors such as

pro-ducer accuracy or user accuracy (Genderen and Lock, 1978) User

accuracy is defined as the ratio of the pixels rightly classified in

each class to pixels in the processed image indexed as the same

class Producer accuracy represents the ratio of rightly classified

pixels in each class to the total pixels located in controlled field

investigations in the considered class In this study, considering

the nature of field studies, the best comparison index for using

controlled classification matrix is producer accuracy In the images

resulted from processing, of the total classified pixels in each class,

10 pixel collections were selected and tested in the field,

micro-scopic and laboratory studies The results are presented as

pro-ducer accuracy matrix (Tables 2 and 4) and user accuracy The

Producer Accuracy of each class showed in blue color inTables 2

and 4 Examination of the values expressed in producer accuracy

tables seems promising So the metamorphic, flysches and

lime-stone which contain more separable spectral pattern from each

other have the higher user accuracies The lowest user accuracies

belong to the completely intermingled part an ophiolite mélange

in which about 20–40 pixels of this lithology are classified

cor-rectly in tow methods Generally, the average producer accuracy

for all five lithological units of SVM and NUT methods are

respec-tively 52% and 65% which is considered as permissible values for

separation of ophiolite mélanges

5 Conclusion

For the first time in this area, we present that advanced

hyper-spectral processing methods could be inexpensive and

advanta-geous tools for distinct units of Ophiolite complexes

We obtained good overall accuracies of 52% and 62% respec-tively for SVM and NNT methods without any extensive field stud-ies; however, NNT results are better than SVM’s

The processing results in the whole of our studies are reason-able, so that in every tow processing method units with minimum disturbing such as limestone units have best correlations with field trainings than others with high disturbing such as mélanges

In the SVM processing method, we obtained the best results with the gamma values of six and polynomial kernel value of three; and in the Neutral network processing method, as better classifica-tion pattern for lithology separaclassifica-tion, we find best results at using 100% of training data, with 50 periods of iterations that obtained least RMS values

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Table 4

Coefficient of user accuracy and producer accuracy on optimal pixel in the NUT image processing method.

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